My research program combines both computational and experimental approaches to map and functionally characterize gene regulatory networks. My long-term aim is to develop data-driven approaches to “reverse engineer” the regulatory networks that control immune responses in host defense against pathogens and in chronic inflammatory diseases. A comprehensive understanding of these networks is a gateway to being able to predict how the immune system will respond to novel therapies, pathogens, and vaccines. On the computational side, I use integrative machine-learning methods to both identify the genomic regulatory elements that mediate transcriptional control in specific cell types, and to leverage information from genetic epidemiology and from molecular networks to uncover novel molecular regulators of inflammatory responses. I am particularly interested in applying state-of-the-art semi-supervised learning algorithms to identify candidate disease genes using features derived from each gene’s local interaction network neighborhood. On the experimental side, I have been studying the mammalian macrophage (a key constituent of the innate immune system) and its roles in atherosclerosis and in host defense, as both a primary application area and a “test-bed” for integrative methods development. My collaborators and I are also employing this computational systems biology approach in studies of gene regulation in other cell types such as smooth muscle cells and cancer cells.
Machine learning, Data mining, Systems biology, Gene regulation